Overview

Dataset statistics

Number of variables15
Number of observations15000
Missing cells0
Missing cells (%)0.0%
Duplicate rows30
Duplicate rows (%)0.2%
Total size in memory1.7 MiB
Average record size in memory120.0 B

Variable types

Numeric8
Categorical7

Alerts

eco_category has constant value "uncategorized"Constant
Dataset has 30 (0.2%) duplicate rowsDuplicates
url has a high cardinality: 14926 distinct valuesHigh cardinality
make has a high cardinality: 109 distinct valuesHigh cardinality
model has a high cardinality: 526 distinct valuesHigh cardinality
reg_date has a high cardinality: 3471 distinct valuesHigh cardinality
manufactured is highly overall correlated with no_of_owners and 1 other fieldsHigh correlation
power is highly overall correlated with engine_cap and 2 other fieldsHigh correlation
engine_cap is highly overall correlated with power and 1 other fieldsHigh correlation
curb_weight is highly overall correlated with power and 2 other fieldsHigh correlation
no_of_owners is highly overall correlated with manufactured and 1 other fieldsHigh correlation
mileage is highly overall correlated with manufactured and 2 other fieldsHigh correlation
price is highly overall correlated with power and 2 other fieldsHigh correlation
transmission is highly imbalanced (87.9%)Imbalance
url is uniformly distributedUniform

Reproduction

Analysis started2023-09-02 16:10:11.682381
Analysis finished2023-09-02 16:10:38.147223
Duration26.46 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

listing_id
Real number (ℝ)

Distinct14926
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011540
Minimum540570
Maximum1031324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:38.394049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum540570
5-th percentile973097.9
Q11004484.2
median1018245.5
Q31025443.8
95-th percentile1030152.1
Maximum1031324
Range490754
Interquartile range (IQR)20959.5

Descriptive statistics

Standard deviation22203.124
Coefficient of variation (CV)0.021949823
Kurtosis36.791099
Mean1011540
Median Absolute Deviation (MAD)8885
Skewness-4.0095874
Sum1.51731 × 1010
Variance4.9297872 × 108
MonotonicityNot monotonic
2023-09-03T00:10:38.933339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014871 3
 
< 0.1%
1022502 2
 
< 0.1%
1021103 2
 
< 0.1%
930900 2
 
< 0.1%
1003291 2
 
< 0.1%
1024879 2
 
< 0.1%
1024194 2
 
< 0.1%
1021331 2
 
< 0.1%
1022814 2
 
< 0.1%
1024684 2
 
< 0.1%
Other values (14916) 14979
99.9%
ValueCountFrequency (%)
540570 1
< 0.1%
691782 1
< 0.1%
708011 1
< 0.1%
733385 1
< 0.1%
743986 1
< 0.1%
749258 1
< 0.1%
756142 1
< 0.1%
759456 1
< 0.1%
767227 1
< 0.1%
773433 1
< 0.1%
ValueCountFrequency (%)
1031324 1
< 0.1%
1031322 1
< 0.1%
1031319 1
< 0.1%
1031316 1
< 0.1%
1031314 1
< 0.1%
1031312 1
< 0.1%
1031310 1
< 0.1%
1031309 1
< 0.1%
1031305 1
< 0.1%
1031304 1
< 0.1%

url
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct14926
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
https://www.sgcarmart.com/listing/1014871
 
3
https://www.sgcarmart.com/listing/1022502
 
2
https://www.sgcarmart.com/listing/1021103
 
2
https://www.sgcarmart.com/listing/930900
 
2
https://www.sgcarmart.com/listing/1003291
 
2
Other values (14921)
14989 

Length

Max length41
Median length41
Mean length40.8038
Min length40

Characters and Unicode

Total characters612057
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14853 ?
Unique (%)99.0%

Sample

1st rowhttps://www.sgcarmart.com/listing/1004862
2nd rowhttps://www.sgcarmart.com/listing/1004953
3rd rowhttps://www.sgcarmart.com/listing/1031276
4th rowhttps://www.sgcarmart.com/listing/1024287
5th rowhttps://www.sgcarmart.com/listing/1023453

Common Values

ValueCountFrequency (%)
https://www.sgcarmart.com/listing/1014871 3
 
< 0.1%
https://www.sgcarmart.com/listing/1022502 2
 
< 0.1%
https://www.sgcarmart.com/listing/1021103 2
 
< 0.1%
https://www.sgcarmart.com/listing/930900 2
 
< 0.1%
https://www.sgcarmart.com/listing/1003291 2
 
< 0.1%
https://www.sgcarmart.com/listing/1024879 2
 
< 0.1%
https://www.sgcarmart.com/listing/1024194 2
 
< 0.1%
https://www.sgcarmart.com/listing/1021331 2
 
< 0.1%
https://www.sgcarmart.com/listing/1022814 2
 
< 0.1%
https://www.sgcarmart.com/listing/1024684 2
 
< 0.1%
Other values (14916) 14979
99.9%

Length

2023-09-03T00:10:39.269474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.sgcarmart.com/listing/1014871 3
 
< 0.1%
https://www.sgcarmart.com/listing/976473 2
 
< 0.1%
https://www.sgcarmart.com/listing/1024837 2
 
< 0.1%
https://www.sgcarmart.com/listing/1004028 2
 
< 0.1%
https://www.sgcarmart.com/listing/979538 2
 
< 0.1%
https://www.sgcarmart.com/listing/1022183 2
 
< 0.1%
https://www.sgcarmart.com/listing/1025482 2
 
< 0.1%
https://www.sgcarmart.com/listing/1021080 2
 
< 0.1%
https://www.sgcarmart.com/listing/1002661 2
 
< 0.1%
https://www.sgcarmart.com/listing/1007612 2
 
< 0.1%
Other values (14916) 14979
99.9%

Most occurring characters

ValueCountFrequency (%)
/ 60000
 
9.8%
t 60000
 
9.8%
s 45000
 
7.4%
w 45000
 
7.4%
i 30000
 
4.9%
. 30000
 
4.9%
g 30000
 
4.9%
c 30000
 
4.9%
a 30000
 
4.9%
r 30000
 
4.9%
Other values (17) 222057
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 405000
66.2%
Other Punctuation 105000
 
17.2%
Decimal Number 102057
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 60000
14.8%
s 45000
11.1%
w 45000
11.1%
i 30000
7.4%
g 30000
7.4%
c 30000
7.4%
a 30000
7.4%
r 30000
7.4%
m 30000
7.4%
h 15000
 
3.7%
Other values (4) 60000
14.8%
Decimal Number
ValueCountFrequency (%)
1 21203
20.8%
0 20385
20.0%
2 11685
11.4%
9 10324
10.1%
8 7014
 
6.9%
3 6756
 
6.6%
7 6467
 
6.3%
6 6261
 
6.1%
5 6032
 
5.9%
4 5930
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 60000
57.1%
. 30000
28.6%
: 15000
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 405000
66.2%
Common 207057
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 60000
14.8%
s 45000
11.1%
w 45000
11.1%
i 30000
7.4%
g 30000
7.4%
c 30000
7.4%
a 30000
7.4%
r 30000
7.4%
m 30000
7.4%
h 15000
 
3.7%
Other values (4) 60000
14.8%
Common
ValueCountFrequency (%)
/ 60000
29.0%
. 30000
14.5%
1 21203
 
10.2%
0 20385
 
9.8%
: 15000
 
7.2%
2 11685
 
5.6%
9 10324
 
5.0%
8 7014
 
3.4%
3 6756
 
3.3%
7 6467
 
3.1%
Other values (3) 18223
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 612057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 60000
 
9.8%
t 60000
 
9.8%
s 45000
 
7.4%
w 45000
 
7.4%
i 30000
 
4.9%
. 30000
 
4.9%
g 30000
 
4.9%
c 30000
 
4.9%
a 30000
 
4.9%
r 30000
 
4.9%
Other values (17) 222057
36.3%

make
Categorical

Distinct109
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
mercedes-benz
1776 
toyota
1760 
honda
1708 
bmw
1438 
audi
 
591
Other values (104)
7727 

Length

Max length13
Median length11
Mean length6.6737333
Min length2

Characters and Unicode

Total characters100106
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.1%

Sample

1st rowLamborghini
2nd rowMitsubishi
3rd rowtoyota
4th rowvolkswagen
5th rowbmw

Common Values

ValueCountFrequency (%)
mercedes-benz 1776
 
11.8%
toyota 1760
 
11.7%
honda 1708
 
11.4%
bmw 1438
 
9.6%
audi 591
 
3.9%
volkswagen 499
 
3.3%
mazda 493
 
3.3%
hyundai 480
 
3.2%
nissan 406
 
2.7%
Mercedes-Benz 394
 
2.6%
Other values (99) 5455
36.4%

Length

2023-09-03T00:10:39.589204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mercedes-benz 2170
14.3%
toyota 2115
14.0%
honda 2091
13.8%
bmw 1761
11.6%
audi 728
 
4.8%
volkswagen 620
 
4.1%
mazda 597
 
3.9%
hyundai 596
 
3.9%
nissan 497
 
3.3%
kia 491
 
3.2%
Other values (57) 3460
22.9%

Most occurring characters

ValueCountFrequency (%)
e 11130
 
11.1%
a 9558
 
9.5%
o 8500
 
8.5%
n 7250
 
7.2%
d 6415
 
6.4%
s 6083
 
6.1%
t 4909
 
4.9%
m 4780
 
4.8%
i 4662
 
4.7%
b 4093
 
4.1%
Other values (35) 32726
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94649
94.5%
Uppercase Letter 3119
 
3.1%
Dash Punctuation 2212
 
2.2%
Space Separator 126
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11130
11.8%
a 9558
 
10.1%
o 8500
 
9.0%
n 7250
 
7.7%
d 6415
 
6.8%
s 6083
 
6.4%
t 4909
 
5.2%
m 4780
 
5.1%
i 4662
 
4.9%
b 4093
 
4.3%
Other values (15) 27269
28.8%
Uppercase Letter
ValueCountFrequency (%)
B 738
23.7%
M 627
20.1%
H 501
16.1%
T 355
11.4%
V 163
 
5.2%
A 143
 
4.6%
S 101
 
3.2%
K 99
 
3.2%
N 91
 
2.9%
L 88
 
2.8%
Other values (8) 213
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 2212
100.0%
Space Separator
ValueCountFrequency (%)
126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 97768
97.7%
Common 2338
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11130
 
11.4%
a 9558
 
9.8%
o 8500
 
8.7%
n 7250
 
7.4%
d 6415
 
6.6%
s 6083
 
6.2%
t 4909
 
5.0%
m 4780
 
4.9%
i 4662
 
4.8%
b 4093
 
4.2%
Other values (33) 30388
31.1%
Common
ValueCountFrequency (%)
- 2212
94.6%
126
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11130
 
11.1%
a 9558
 
9.5%
o 8500
 
8.5%
n 7250
 
7.2%
d 6415
 
6.4%
s 6083
 
6.1%
t 4909
 
4.9%
m 4780
 
4.8%
i 4662
 
4.7%
b 4093
 
4.1%
Other values (35) 32726
32.7%

model
Categorical

Distinct526
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
amg
 
537
corolla
 
446
vezel
 
401
cerato
 
349
c180
 
341
Other values (521)
12926 

Length

Max length13
Median length11
Mean length4.6754
Min length1

Characters and Unicode

Total characters70131
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)0.7%

Sample

1st rowgallardo
2nd rowattrage
3rd rowvios
4th rowgolf
5th row216d

Common Values

ValueCountFrequency (%)
amg 537
 
3.6%
corolla 446
 
3.0%
vezel 401
 
2.7%
cerato 349
 
2.3%
c180 341
 
2.3%
civic 330
 
2.2%
2 329
 
2.2%
fit 277
 
1.8%
3 258
 
1.7%
jazz 254
 
1.7%
Other values (516) 11478
76.5%

Length

2023-09-03T00:10:39.912988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
amg 537
 
3.6%
corolla 446
 
3.0%
vezel 401
 
2.7%
cerato 349
 
2.3%
c180 341
 
2.3%
civic 330
 
2.2%
2 329
 
2.2%
fit 277
 
1.8%
3 258
 
1.7%
jazz 254
 
1.7%
Other values (516) 11478
76.5%

Most occurring characters

ValueCountFrequency (%)
a 7363
 
10.5%
e 5776
 
8.2%
r 4868
 
6.9%
i 4651
 
6.6%
c 4231
 
6.0%
o 3912
 
5.6%
l 3877
 
5.5%
t 3701
 
5.3%
s 3128
 
4.5%
0 3074
 
4.4%
Other values (27) 25550
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57866
82.5%
Decimal Number 11896
 
17.0%
Dash Punctuation 369
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7363
12.7%
e 5776
10.0%
r 4868
 
8.4%
i 4651
 
8.0%
c 4231
 
7.3%
o 3912
 
6.8%
l 3877
 
6.7%
t 3701
 
6.4%
s 3128
 
5.4%
n 2477
 
4.3%
Other values (16) 13882
24.0%
Decimal Number
ValueCountFrequency (%)
0 3074
25.8%
2 1804
15.2%
1 1756
14.8%
3 1430
12.0%
8 1169
 
9.8%
5 1066
 
9.0%
4 644
 
5.4%
6 629
 
5.3%
7 182
 
1.5%
9 142
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57866
82.5%
Common 12265
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7363
12.7%
e 5776
10.0%
r 4868
 
8.4%
i 4651
 
8.0%
c 4231
 
7.3%
o 3912
 
6.8%
l 3877
 
6.7%
t 3701
 
6.4%
s 3128
 
5.4%
n 2477
 
4.3%
Other values (16) 13882
24.0%
Common
ValueCountFrequency (%)
0 3074
25.1%
2 1804
14.7%
1 1756
14.3%
3 1430
11.7%
8 1169
 
9.5%
5 1066
 
8.7%
4 644
 
5.3%
6 629
 
5.1%
- 369
 
3.0%
7 182
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7363
 
10.5%
e 5776
 
8.2%
r 4868
 
6.9%
i 4651
 
6.6%
c 4231
 
6.0%
o 3912
 
5.6%
l 3877
 
5.5%
t 3701
 
5.3%
s 3128
 
4.5%
0 3074
 
4.4%
Other values (27) 25550
36.4%

manufactured
Real number (ℝ)

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.7358
Minimum1933
Maximum2121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:40.251074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1933
5-th percentile2008
Q12012
median2016
Q32018
95-th percentile2020
Maximum2121
Range188
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.484561
Coefficient of variation (CV)0.0071821809
Kurtosis39.083433
Mean2016.7358
Median Absolute Deviation (MAD)2
Skewness6.0899714
Sum30251037
Variance209.80252
MonotonicityNot monotonic
2023-09-03T00:10:40.688569image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2016 2194
14.6%
2018 1872
12.5%
2017 1722
11.5%
2019 1536
10.2%
2015 1412
9.4%
2011 941
 
6.3%
2008 932
 
6.2%
2009 692
 
4.6%
2014 672
 
4.5%
2020 663
 
4.4%
Other values (35) 2364
15.8%
ValueCountFrequency (%)
1933 1
 
< 0.1%
1962 1
 
< 0.1%
1964 1
 
< 0.1%
1969 1
 
< 0.1%
1972 1
 
< 0.1%
1975 1
 
< 0.1%
1976 1
 
< 0.1%
1978 2
< 0.1%
1982 1
 
< 0.1%
2003 3
< 0.1%
ValueCountFrequency (%)
2121 2
 
< 0.1%
2120 11
 
0.1%
2119 37
0.2%
2118 37
0.2%
2117 33
0.2%
2116 45
0.3%
2115 27
0.2%
2114 14
 
0.1%
2113 8
 
0.1%
2112 10
 
0.1%

reg_date
Categorical

Distinct3471
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
29-dec-2017
 
36
28-feb-2017
 
35
30-nov-2018
 
33
30-jun-2016
 
31
29-aug-2017
 
30
Other values (3466)
14835 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters165000
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique810 ?
Unique (%)5.4%

Sample

1st row06-jan-2012
2nd row31-jul-2017
3rd row21-nov-2018
4th row18-nov-2016
5th row24-nov-2016

Common Values

ValueCountFrequency (%)
29-dec-2017 36
 
0.2%
28-feb-2017 35
 
0.2%
30-nov-2018 33
 
0.2%
30-jun-2016 31
 
0.2%
29-aug-2017 30
 
0.2%
30-nov-2016 28
 
0.2%
29-jun-2018 28
 
0.2%
31-may-2016 26
 
0.2%
31-dec-2018 26
 
0.2%
28-jun-2018 26
 
0.2%
Other values (3461) 14701
98.0%

Length

2023-09-03T00:10:41.073987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29-dec-2017 36
 
0.2%
28-feb-2017 35
 
0.2%
30-nov-2018 33
 
0.2%
30-jun-2016 31
 
0.2%
29-aug-2017 30
 
0.2%
30-nov-2016 28
 
0.2%
29-jun-2018 28
 
0.2%
31-may-2016 26
 
0.2%
31-dec-2018 26
 
0.2%
28-jun-2018 26
 
0.2%
Other values (3461) 14701
98.0%

Most occurring characters

ValueCountFrequency (%)
- 30000
18.2%
2 24345
14.8%
0 23241
14.1%
1 19590
11.9%
a 6146
 
3.7%
8 4174
 
2.5%
9 4026
 
2.4%
n 3952
 
2.4%
u 3923
 
2.4%
j 3871
 
2.3%
Other values (20) 41732
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90000
54.5%
Lowercase Letter 45000
27.3%
Dash Punctuation 30000
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6146
13.7%
n 3952
 
8.8%
u 3923
 
8.7%
j 3871
 
8.6%
e 3633
 
8.1%
c 2632
 
5.8%
o 2617
 
5.8%
m 2490
 
5.5%
p 2409
 
5.4%
r 2360
 
5.2%
Other values (9) 10967
24.4%
Decimal Number
ValueCountFrequency (%)
2 24345
27.1%
0 23241
25.8%
1 19590
21.8%
8 4174
 
4.6%
9 4026
 
4.5%
7 3716
 
4.1%
6 3579
 
4.0%
3 3104
 
3.4%
5 2506
 
2.8%
4 1719
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
72.7%
Latin 45000
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6146
13.7%
n 3952
 
8.8%
u 3923
 
8.7%
j 3871
 
8.6%
e 3633
 
8.1%
c 2632
 
5.8%
o 2617
 
5.8%
m 2490
 
5.5%
p 2409
 
5.4%
r 2360
 
5.2%
Other values (9) 10967
24.4%
Common
ValueCountFrequency (%)
- 30000
25.0%
2 24345
20.3%
0 23241
19.4%
1 19590
16.3%
8 4174
 
3.5%
9 4026
 
3.4%
7 3716
 
3.1%
6 3579
 
3.0%
3 3104
 
2.6%
5 2506
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 30000
18.2%
2 24345
14.8%
0 23241
14.1%
1 19590
11.9%
a 6146
 
3.7%
8 4174
 
2.5%
9 4026
 
2.4%
n 3952
 
2.4%
u 3923
 
2.4%
j 3871
 
2.3%
Other values (20) 41732
25.3%

type_of_vehicle
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
suv
3375 
luxury sedan
2817 
mid-sized sedan
2298 
hatchback
1927 
mpv
1732 
Other values (8)
2851 

Length

Max length15
Median length12
Mean length8.8024
Min length3

Characters and Unicode

Total characters132036
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsports car
2nd rowmid-sized_sedan
3rd rowmid-sized sedan
4th rowhatchback
5th rowmpv

Common Values

ValueCountFrequency (%)
suv 3375
22.5%
luxury sedan 2817
18.8%
mid-sized sedan 2298
15.3%
hatchback 1927
12.8%
mpv 1732
11.5%
sports car 1715
11.4%
stationwagon 345
 
2.3%
luxury_sedan 324
 
2.2%
mid-sized_sedan 254
 
1.7%
sports_car 206
 
1.4%
Other values (3) 7
 
< 0.1%

Length

2023-09-03T00:10:41.358141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sedan 5115
23.4%
suv 3375
15.5%
luxury 2817
12.9%
mid-sized 2298
10.5%
hatchback 1927
 
8.8%
mpv 1732
 
7.9%
sports 1715
 
7.9%
car 1715
 
7.9%
stationwagon 345
 
1.6%
luxury_sedan 324
 
1.5%
Other values (5) 467
 
2.1%

Most occurring characters

ValueCountFrequency (%)
s 15809
 
12.0%
a 12159
 
9.2%
d 10797
 
8.2%
u 9664
 
7.3%
e 8245
 
6.2%
r 6983
 
5.3%
6830
 
5.2%
n 6400
 
4.8%
c 5775
 
4.4%
i 5451
 
4.1%
Other values (17) 43923
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121868
92.3%
Space Separator 6830
 
5.2%
Dash Punctuation 2552
 
1.9%
Connector Punctuation 785
 
0.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 15809
13.0%
a 12159
 
10.0%
d 10797
 
8.9%
u 9664
 
7.9%
e 8245
 
6.8%
r 6983
 
5.7%
n 6400
 
5.3%
c 5775
 
4.7%
i 5451
 
4.5%
v 5108
 
4.2%
Other values (13) 35477
29.1%
Space Separator
ValueCountFrequency (%)
6830
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2552
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 785
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121868
92.3%
Common 10168
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 15809
13.0%
a 12159
 
10.0%
d 10797
 
8.9%
u 9664
 
7.9%
e 8245
 
6.8%
r 6983
 
5.7%
n 6400
 
5.3%
c 5775
 
4.7%
i 5451
 
4.5%
v 5108
 
4.2%
Other values (13) 35477
29.1%
Common
ValueCountFrequency (%)
6830
67.2%
- 2552
 
25.1%
_ 785
 
7.7%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 15809
 
12.0%
a 12159
 
9.2%
d 10797
 
8.2%
u 9664
 
7.3%
e 8245
 
6.2%
r 6983
 
5.3%
6830
 
5.2%
n 6400
 
4.8%
c 5775
 
4.4%
i 5451
 
4.1%
Other values (17) 43923
33.3%

eco_category
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
uncategorized
15000 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters195000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuncategorized
2nd rowuncategorized
3rd rowuncategorized
4th rowuncategorized
5th rowuncategorized

Common Values

ValueCountFrequency (%)
uncategorized 15000
100.0%

Length

2023-09-03T00:10:41.686040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T00:10:42.251424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
uncategorized 15000
100.0%

Most occurring characters

ValueCountFrequency (%)
e 30000
15.4%
u 15000
7.7%
n 15000
7.7%
c 15000
7.7%
a 15000
7.7%
t 15000
7.7%
g 15000
7.7%
o 15000
7.7%
r 15000
7.7%
i 15000
7.7%
Other values (2) 30000
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 195000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30000
15.4%
u 15000
7.7%
n 15000
7.7%
c 15000
7.7%
a 15000
7.7%
t 15000
7.7%
g 15000
7.7%
o 15000
7.7%
r 15000
7.7%
i 15000
7.7%
Other values (2) 30000
15.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 195000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30000
15.4%
u 15000
7.7%
n 15000
7.7%
c 15000
7.7%
a 15000
7.7%
t 15000
7.7%
g 15000
7.7%
o 15000
7.7%
r 15000
7.7%
i 15000
7.7%
Other values (2) 30000
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30000
15.4%
u 15000
7.7%
n 15000
7.7%
c 15000
7.7%
a 15000
7.7%
t 15000
7.7%
g 15000
7.7%
o 15000
7.7%
r 15000
7.7%
i 15000
7.7%
Other values (2) 30000
15.4%

transmission
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
auto
14753 
manual
 
247

Length

Max length6
Median length4
Mean length4.0329333
Min length4

Characters and Unicode

Total characters60494
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowauto
2nd rowauto
3rd rowauto
4th rowauto
5th rowauto

Common Values

ValueCountFrequency (%)
auto 14753
98.4%
manual 247
 
1.6%

Length

2023-09-03T00:10:42.549485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T00:10:42.891596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
auto 14753
98.4%
manual 247
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 15247
25.2%
u 15000
24.8%
t 14753
24.4%
o 14753
24.4%
m 247
 
0.4%
n 247
 
0.4%
l 247
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60494
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15247
25.2%
u 15000
24.8%
t 14753
24.4%
o 14753
24.4%
m 247
 
0.4%
n 247
 
0.4%
l 247
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 60494
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15247
25.2%
u 15000
24.8%
t 14753
24.4%
o 14753
24.4%
m 247
 
0.4%
n 247
 
0.4%
l 247
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15247
25.2%
u 15000
24.8%
t 14753
24.4%
o 14753
24.4%
m 247
 
0.4%
n 247
 
0.4%
l 247
 
0.4%

power
Real number (ℝ)

Distinct258
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.0206
Minimum38
Maximum735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:43.211223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile73
Q190
median110
Q3150
95-th percentile309
Maximum735
Range697
Interquartile range (IQR)60

Descriptive statistics

Standard deviation76.052745
Coefficient of variation (CV)0.55912667
Kurtosis6.0583024
Mean136.0206
Median Absolute Deviation (MAD)25
Skewness2.330448
Sum2040309
Variance5784.02
MonotonicityNot monotonic
2023-09-03T00:10:43.572262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 911
 
6.1%
135 679
 
4.5%
96 604
 
4.0%
115 584
 
3.9%
85 543
 
3.6%
80 493
 
3.3%
88 443
 
3.0%
73 428
 
2.9%
100 422
 
2.8%
110 402
 
2.7%
Other values (248) 9491
63.3%
ValueCountFrequency (%)
38 1
 
< 0.1%
40 2
 
< 0.1%
45 5
 
< 0.1%
47 20
0.1%
48 3
 
< 0.1%
50 2
 
< 0.1%
51 2
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
57 27
0.2%
ValueCountFrequency (%)
735 1
 
< 0.1%
552 3
 
< 0.1%
545 2
 
< 0.1%
541 4
 
< 0.1%
533 2
 
< 0.1%
530 9
0.1%
515 13
0.1%
507 1
 
< 0.1%
493 9
0.1%
489 1
 
< 0.1%

engine_cap
Real number (ℝ)

Distinct228
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.1645
Minimum647
Maximum6752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:43.973711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile1199
Q11497
median1598
Q31998
95-th percentile3745
Maximum6752
Range6105
Interquartile range (IQR)501

Descriptive statistics

Standard deviation827.09375
Coefficient of variation (CV)0.41895889
Kurtosis8.2913178
Mean1974.1645
Median Absolute Deviation (MAD)266
Skewness2.5607509
Sum29612467
Variance684084.08
MonotonicityNot monotonic
2023-09-03T00:10:44.333466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1496 1576
 
10.5%
1998 1102
 
7.3%
1591 811
 
5.4%
1598 759
 
5.1%
1595 694
 
4.6%
1991 514
 
3.4%
1499 462
 
3.1%
1997 454
 
3.0%
1984 445
 
3.0%
1498 326
 
2.2%
Other values (218) 7857
52.4%
ValueCountFrequency (%)
647 1
 
< 0.1%
658 2
 
< 0.1%
659 4
 
< 0.1%
796 1
 
< 0.1%
988 5
 
< 0.1%
989 6
 
< 0.1%
996 10
 
0.1%
998 47
 
0.3%
999 184
1.2%
1086 17
 
0.1%
ValueCountFrequency (%)
6752 6
 
< 0.1%
6749 13
0.1%
6592 30
0.2%
6498 18
0.1%
6262 6
 
< 0.1%
6208 9
 
0.1%
5999 3
 
< 0.1%
5998 32
0.2%
5980 3
 
< 0.1%
5950 12
 
0.1%

curb_weight
Real number (ℝ)

Distinct645
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1472.0349
Minimum2
Maximum2905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:44.726177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1067.95
Q11280
median1430
Q31625
95-th percentile1990
Maximum2905
Range2903
Interquartile range (IQR)345

Descriptive statistics

Standard deviation284.10088
Coefficient of variation (CV)0.19299874
Kurtosis1.2402494
Mean1472.0349
Median Absolute Deviation (MAD)180
Skewness0.83745408
Sum22080523
Variance80713.311
MonotonicityNot monotonic
2023-09-03T00:10:45.116519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1190 378
 
2.5%
1430 373
 
2.5%
1425 258
 
1.7%
1615 243
 
1.6%
1345 231
 
1.5%
1480 225
 
1.5%
1610 208
 
1.4%
1195 185
 
1.2%
1310 182
 
1.2%
1285 180
 
1.2%
Other values (635) 12537
83.6%
ValueCountFrequency (%)
2 1
 
< 0.1%
780 1
 
< 0.1%
786 1
 
< 0.1%
795 1
 
< 0.1%
800 3
< 0.1%
805 1
 
< 0.1%
806 1
 
< 0.1%
830 1
 
< 0.1%
840 3
< 0.1%
852 4
< 0.1%
ValueCountFrequency (%)
2905 1
 
< 0.1%
2815 5
< 0.1%
2760 2
 
< 0.1%
2745 2
 
< 0.1%
2730 1
 
< 0.1%
2685 2
 
< 0.1%
2635 3
< 0.1%
2625 2
 
< 0.1%
2600 2
 
< 0.1%
2590 1
 
< 0.1%

no_of_owners
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2975.1687
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:45.443559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum99999
Range99998
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16985.14
Coefficient of variation (CV)5.7089671
Kurtosis28.672888
Mean2975.1687
Median Absolute Deviation (MAD)0
Skewness5.5379658
Sum44627531
Variance2.88495 × 108
MonotonicityNot monotonic
2023-09-03T00:10:45.722935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 7540
50.3%
2 3562
23.7%
3 1702
 
11.3%
4 917
 
6.1%
5 459
 
3.1%
99999 446
 
3.0%
6 374
 
2.5%
ValueCountFrequency (%)
1 7540
50.3%
2 3562
23.7%
3 1702
 
11.3%
4 917
 
6.1%
5 459
 
3.1%
6 374
 
2.5%
99999 446
 
3.0%
ValueCountFrequency (%)
99999 446
 
3.0%
6 374
 
2.5%
5 459
 
3.1%
4 917
 
6.1%
3 1702
 
11.3%
2 3562
23.7%
1 7540
50.3%

mileage
Real number (ℝ)

Distinct5179
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73729.744
Minimum1
Maximum386000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:46.090928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7000
Q135306.75
median67897.5
Q3105000
95-th percentile160000
Maximum386000
Range385999
Interquartile range (IQR)69693.25

Descriptive statistics

Standard deviation48326.853
Coefficient of variation (CV)0.65545939
Kurtosis0.41689662
Mean73729.744
Median Absolute Deviation (MAD)34102.5
Skewness0.69655955
Sum1.1059462 × 109
Variance2.3354847 × 109
MonotonicityNot monotonic
2023-09-03T00:10:46.563186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80000 151
 
1.0%
120000 124
 
0.8%
50000 120
 
0.8%
60000 114
 
0.8%
90000 113
 
0.8%
130000 113
 
0.8%
110000 103
 
0.7%
70000 102
 
0.7%
140000 99
 
0.7%
65000 96
 
0.6%
Other values (5169) 13865
92.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 10
0.1%
12 1
 
< 0.1%
14 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 5
< 0.1%
ValueCountFrequency (%)
386000 1
< 0.1%
340000 1
< 0.1%
330000 1
< 0.1%
319000 1
< 0.1%
318000 1
< 0.1%
311000 1
< 0.1%
300000 2
< 0.1%
298223 1
< 0.1%
290000 1
< 0.1%
288000 1
< 0.1%

price
Real number (ℝ)

Distinct2660
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108264.32
Minimum1900
Maximum2388777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:10:47.100463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile21552.25
Q157800
median78000
Q3119900
95-th percentile274988.6
Maximum2388777
Range2386877
Interquartile range (IQR)62100

Descriptive statistics

Standard deviation112229.23
Coefficient of variation (CV)1.0366226
Kurtosis47.252703
Mean108264.32
Median Absolute Deviation (MAD)26200
Skewness5.3027808
Sum1.6239647 × 109
Variance1.2595401 × 1010
MonotonicityNot monotonic
2023-09-03T00:10:47.533019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69800 177
 
1.2%
65800 147
 
1.0%
72800 131
 
0.9%
79800 127
 
0.8%
58800 127
 
0.8%
66800 127
 
0.8%
75800 126
 
0.8%
76800 123
 
0.8%
59800 121
 
0.8%
68800 120
 
0.8%
Other values (2650) 13674
91.2%
ValueCountFrequency (%)
1900 1
 
< 0.1%
3500 1
 
< 0.1%
3999 1
 
< 0.1%
4800 2
< 0.1%
5000 1
 
< 0.1%
5300 1
 
< 0.1%
5500 3
< 0.1%
5800 2
< 0.1%
6000 3
< 0.1%
6300 1
 
< 0.1%
ValueCountFrequency (%)
2388777 1
< 0.1%
2000000 1
< 0.1%
1700000 1
< 0.1%
1535555 1
< 0.1%
1480000 1
< 0.1%
1398888 1
< 0.1%
1390900 1
< 0.1%
1380500 1
< 0.1%
1330000 1
< 0.1%
1300000 1
< 0.1%

Interactions

2023-09-03T00:10:34.258393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:13.933100image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:16.706841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:19.672350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:22.490708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:25.420460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:28.086627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:31.323224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:34.576874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:14.284512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:17.027945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:19.993504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:22.763366image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:25.781268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:29.047661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:31.713232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:34.936509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:14.634137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:17.391078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:20.356172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:23.081835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:26.114277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:29.382553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:32.054481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:35.234342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:14.974332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:17.716280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:20.726238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:23.453066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:26.487413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:29.717789image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:32.495016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:35.517737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:15.334238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:18.116322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:21.057708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:23.833976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:26.796312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:30.062903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:32.890419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:35.812446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:15.623857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:18.504205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:21.388038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:24.297923image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:27.091744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:30.375072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:33.229473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:36.127966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:15.947930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:18.853097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:21.728152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:24.725154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:27.393807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:30.695879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:33.595863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:36.524559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:16.382239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:19.240688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:22.115292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:25.085295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:27.744407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:31.001059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:10:33.946118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-09-03T00:10:47.845434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
listing_idmanufacturedpowerengine_capcurb_weightno_of_ownersmileagepricetype_of_vehicletransmission
listing_id1.000-0.028-0.068-0.063-0.0580.0110.048-0.0830.0260.018
manufactured-0.0281.000-0.048-0.1860.027-0.616-0.7930.4640.3550.221
power-0.068-0.0481.0000.8140.8010.105-0.0640.6680.2850.052
engine_cap-0.063-0.1860.8141.0000.7790.1740.0770.4900.2520.047
curb_weight-0.0580.0270.8010.7791.0000.012-0.0760.6320.2670.136
no_of_owners0.011-0.6160.1050.1740.0121.0000.539-0.2230.0000.000
mileage0.048-0.793-0.0640.077-0.0760.5391.000-0.5510.1250.175
price-0.0830.4640.6680.4900.632-0.223-0.5511.0000.1200.000
type_of_vehicle0.0260.3550.2850.2520.2670.0000.1250.1201.0000.212
transmission0.0180.2210.0520.0470.1360.0000.1750.0000.2121.000

Missing values

2023-09-03T00:10:37.079331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-03T00:10:37.807669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

listing_idurlmakemodelmanufacturedreg_datetype_of_vehicleeco_categorytransmissionpowerengine_capcurb_weightno_of_ownersmileageprice
01004862https://www.sgcarmart.com/listing/1004862Lamborghinigallardo201106-jan-2012sports caruncategorizedauto41252041500549000362800
11004953https://www.sgcarmart.com/listing/1004953Mitsubishiattrage201631-jul-2017mid-sized_sedanuncategorizedauto57119394029000043800
21031276https://www.sgcarmart.com/listing/1031276toyotavios201821-nov-2018mid-sized sedanuncategorizedauto791496108512027056666
31024287https://www.sgcarmart.com/listing/1024287volkswagengolf201618-nov-2016hatchbackuncategorizedauto811197122916000054200
41023453https://www.sgcarmart.com/listing/1023453bmw216d201624-nov-2016mpvuncategorizedauto8514961480280000103000
51015705https://www.sgcarmart.com/listing/1015705mazda2201931-jan-2020luxury sedanuncategorizedauto1211998151518896104800
61030192https://www.sgcarmart.com/listing/1030192toyotacorolla201609-dec-2016mid-sized sedanuncategorizedauto901598120518602661800
7997557https://www.sgcarmart.com/listing/997557hondashuttle201620-apr-2017stationwagonuncategorizedauto971496113024600060900
81030697https://www.sgcarmart.com/listing/1030697Hyundaielantra201107-oct-2011mid-sized sedanuncategorizedauto951591126736872060800
91015102https://www.sgcarmart.com/listing/1015102hondajazz201913-may-2019hatchbackuncategorizedauto961498108811670070000
listing_idurlmakemodelmanufacturedreg_datetype_of_vehicleeco_categorytransmissionpowerengine_capcurb_weightno_of_ownersmileageprice
149901027545https://www.sgcarmart.com/listing/1027545mitsubishioutlander211604-nov-2016suvuncategorizedauto1232360153024200078500
14991990149https://www.sgcarmart.com/listing/990149minicooper201906-jan-2020sports caruncategorizedauto14119981535223000148000
14992984126https://www.sgcarmart.com/listing/984126minicooper201220-sep-2012sports caruncategorizedauto13515981405665663128000
14993981118https://www.sgcarmart.com/listing/981118Hyundaiavante201919-aug-2019mid-sized sedanuncategorizedauto931591134514000072000
149941012698https://www.sgcarmart.com/listing/1012698toyotacorolla201617-apr-2017mid-sized sedanuncategorizedauto901598121514500063800
149951014803https://www.sgcarmart.com/listing/1014803maseratigranturismo200911-dec-2009sports caruncategorizedauto323469118809999990000139500
149961010282https://www.sgcarmart.com/listing/1010282bmwx3201729-jun-2017suvuncategorizedauto13519971680168000121800
149971006206https://www.sgcarmart.com/listing/1006206toyotaharrier201609-feb-2017suvuncategorizedauto1111986161015600084800
14998982384https://www.sgcarmart.com/listing/982384Lexuses201530-nov-2015luxury sedanuncategorizedauto1352494161528500092800
149991028062https://www.sgcarmart.com/listing/1028062bmw530i201717-oct-2017luxury sedanuncategorizedauto18519981615178000147800

Duplicate rows

Most frequently occurring

listing_idurlmakemodelmanufacturedreg_datetype_of_vehicleeco_categorytransmissionpowerengine_capcurb_weightno_of_ownersmileageprice# duplicates
0894308https://www.sgcarmart.com/listing/894308kiasportage201728-mar-2017suvuncategorizedauto11419991500153708778002
1954364https://www.sgcarmart.com/listing/954364mitsubishioutlander201718-dec-2017suvuncategorizedauto12323601535157271788002
2976473https://www.sgcarmart.com/listing/976473subaruforester201628-oct-2016suvuncategorizedauto17719981682193428748002
3981956https://www.sgcarmart.com/listing/981956lexusrx201128-oct-2011suvuncategorizedauto138267218403118200898002
4986149https://www.sgcarmart.com/listing/986149lexusis201919-feb-2020luxury sedanuncategorizedauto180199816202100001498002
5991111https://www.sgcarmart.com/listing/991111kiacerato201831-oct-2018mid-sized sedanuncategorizedauto9315911287130600758002
6991776https://www.sgcarmart.com/listing/991776hyundaiavante201915-aug-2019mid-sized sedanuncategorizedauto9315911345130000718002
7992077https://www.sgcarmart.com/listing/992077Hondavezel201812-jul-2019suvuncategorizedauto9614961190131075828002
8995501https://www.sgcarmart.com/listing/995501lexusrx201931-aug-2019suvuncategorizedauto175199818901310001898002
9995581https://www.sgcarmart.com/listing/995581bmw318i201815-nov-2018luxury sedanuncategorizedauto100149914251700001198002